# Morphset:Augmenting categorical emotion datasets with dimensional affect labels using face morphing

arXiv: https://arxiv.org/abs/2103.02854 · HTML: https://mlsanity.com/paper/2103.02854/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 4 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [GANimation: Anatomically-aware Facial Animation from a Single Image](https://mlsanity.com/paper/1807.09251/) (2018)
- [ExprGAN: Facial Expression Editing with Controllable Expression Intensity](https://mlsanity.com/paper/1709.03842/) (2017)
- [SliderGAN: Synthesizing Expressive Face Images by Sliding 3D Blendshape Parameters](https://mlsanity.com/paper/1908.09638/) (2019)
- [Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives](https://mlsanity.com/paper/1809.08927/) (2018)

## Built on by (most cited first, 4 of 4)

- [MaxEnt Loss: Constrained Maximum Entropy for Calibration under Out-of-Distribution Shift](https://mlsanity.com/paper/2310.17159/) (2023)
- [Efficient Facial Expression Analysis For Dimensional Affect Recognition Using Geometric Features](https://mlsanity.com/paper/2106.07817/) (2021)
- [Underage Detection through a Multi-Task and MultiAge Approach for Screening Minors in Unconstrained Imagery](https://mlsanity.com/paper/2506.10689/) (2025)
- [Generating near-infrared facial expression datasets with dimensional affect labels](https://mlsanity.com/paper/2206.13887/) (2022)

## Abstract (arXiv)

Emotion recognition and understanding is a vital component in human-machine interaction. Dimensional models of affect such as those using valence and arousal have advantages over traditional categorical ones due to the complexity of emotional states in humans. However, dimensional emotion annotations are difficult and expensive to collect, therefore they are not as prevalent in the affective computing community. To address these issues, we propose a method to generate synthetic images from existing categorical emotion datasets using face morphing as well as dimensional labels in the circumplex space with full control over the resulting sample distribution, while achieving augmentation factors of at least 20x or more.